Information processing apparatus and method, computer program thereof, and recording medium

ABSTRACT

An information processing apparatus is provided in which a first record control means controls record of user presence time information indicating time in which a user was present within a predetermined range around a display means displaying content, based on an output from a sensor detecting the user; a second record control means controls record of operation history information including content specifying information for specifying the content as target of operation, operation content information for indicating content of operation related to display of the content by the user, and operation time information for indicating time of the operation; and an audience-quality constituent item calculation means calculates an audience-quality constituent item constituting audience quality which indicates quality of content watched by the user, based on the recorded user presence time information and operation history information.

CROSS REFERENCE TO RELATED APPLICATIONS

The present document is based on Japanese Priority DocumentJP2004-113284, filed to the Japanese Patent Office on Apr. 7, 2004, thecontents of which being incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to information processing apparatus andmethod, a computer program thereof, and a recording medium, inparticular to information processing apparatus and method, a computerprogram thereof, and a recording medium, which do not place a burden toa user, but acquire a degree of expectation, a degree of concentration,and a degree of satisfaction with respect to a program, so as to measurean audience quality with higher efficiency.

2. Description of Related Art

In recent years, in addition to a conventional audience rate, theaudience quality showing how a TV program is watched by viewers isintroduced as a measure of evaluation of a TV program. Examples ofaudience quality investigation may be Research Q (seehttp://www.rq-tv.com/) carried out jointly by TV Asahi and KeioUniversity, a viewer's satisfaction degree survey PASS (Fuji TelevisionAudience Satisfaction Survey) conducted by Fuji Television Network, Inc.etc. However, both integration methods measure audience qualities basedon a questionnaire conducted to a viewer after watching, and so needconsiderable time and labor. Then, a method of finding audience qualityof a watched program is proposed by using an algorithm which finds theaudience quality by approximation (for example, see Japanese Laid-openPatent Application No. 2003-354356 or Japanese Laid-open PatentApplication No. 2003-111106).

According to an invention disclosed in Japanese Laid-open PatentApplication No. 2003-354356, a user's operation history is analyzed by aprogram selection support apparatus. When a particular program iswatched for a certain period of time, a dialog into which the userinputs an evaluation of the program is displayed. Based on the user'sevaluation inputted into the dialog, user's interest (audience quality)in the program is measured.

According to an invention disclosed in Japanese Laid-open Patent.Application No. 2003-111106, based on vital reactions (skin dielectricconstant, skin temperature, pulse) acquired through a sensor worn by aviewer, a degree of concentration (audience quality) with respect to aprogram being watched by the viewer is measured.

However, according to the technology disclosed in Japanese Laid-openPatent Application No. 2003-354356, the viewer has to input theevaluation into the dialog. As a result, there is an inconvenience inthat a burden is placed on the viewer.

Furthermore, according to the technology disclosed in Japanese Laid-openPatent Application No. 2003-354356, although it is possible to acquire adegree of satisfaction, which is an evaluation value after watching theprogram, among items which constitute the audience quality, there is aproblem that a degree of expectation which is an evaluation value beforewatching the program, or a degree of concentration which is anevaluation value during watching the program, cannot be measured.

Further, according to the technology disclosed in Japanese Laid-openPatent Application No. 2003-111106, the viewer has to wear the sensorevery time and this may constitute a cumbersome operation for the user.Furthermore, according to the technology disclosed in Japanese Laid-openPatent Application No. 2003-111106, although it is possible to acquirethe degree of concentration, which is the evaluation value duringwatching the program, among the items which constitute the audiencequality, there may be a problem in that the degree of expectation whichis the evaluation value before watching the program, or the degree ofsatisfaction which is the evaluation value after watching the program,cannot be measured.

SUMMARY OF THE INVENTION

In view of the above situation, the present invention has been conceivedso as not to place a burden on a user, but to acquire a degree ofexpectation and a degree of concentration, and a degree of satisfactionwith respect to a program, and more efficiently measure an audiencequality.

An information processing apparatus in accordance with a preferredembodiment of the present invention includes: a first record controlmeans for controlling record of user presence time informationindicating time in which a user was present within a predetermined rangearound a display means displaying content, based on an output from asensor detecting the user; a second record control means for controllingrecord of operation history information including content specifyinginformation for specifying the content as target of operation, operationcontent information for indicating content of operation related todisplay of the content by the user, and operation time information forindicating time of the operation; and audience-quality constituent itemcalculation means for calculating an audience-quality constituent itemconstituting audience quality which indicates quality of content watchedby the user, based on the recorded user presence time information andoperation history information.

It is preferable to have the information processing apparatus furtherincluding a third record control means for acquiring the contentspecifying information for specifying broadcast content, and EPGinformation containing broadcast start time and broadcast end time ofthe content, and controlling record of the EPG information; wherein: theaudience-quality constituent item calculation means calculates theaudience-quality constituent item of the broadcast content, based on theuser presence time information, the operation history information andthe EPG information.

An information processing apparatus according a preferred embodiment ofthe present invention further may further include an audience qualitygeneration means for generating data of the audience quality, based on avalue of the audience-quality constituent item calculated by theaudience-quality constituent item calculation means.

In an information processing apparatus according a preferred embodimentof the present invention, the audience-quality constituent itemcalculation means may include at least a degree-of-expectationcalculation means for calculating a degree of expectation indicating adegree of the user's expectation regarding the content; adegree-of-concentration calculation means for calculating a degree ofconcentration indicating a degree of concentration of the user whenwatching the content; and a degree-of-satisfaction calculation means forcalculating a degree of satisfaction indicating a degree of satisfactionof the user having watched the content.

In an information processing apparatus according a preferred embodimentof the present invention, the audience quality generation means maystore data of the calculated audience quality into an audience qualitydatabase, and the data of the audience quality stored in the audiencequality database is transmitted to another apparatus via a network at apreset timing.

An information processing apparatus according a preferred embodiment ofthe present invention may further include deletion means for deletingunnecessary information among the user presence time information,operation history information, EPG information, and information of theaudience quality database.

An information-processing method according a preferred embodiment of thepresent invention may include: a first recording step of recording userpresence time information indicating time in which a user was presentwithin a predetermined range around a display means displaying content,based on an output from a sensor detecting the user; a second recordingstep of recording operation history information including contentspecifying information for specifying the content as target ofoperation, operation content information for indicating content ofoperation related to display of the content by the user, and operationtime information for indicating time of the operation; andaudience-quality constituent item calculation step of calculating anaudience-quality constituent item constituting audience quality whichindicates quality of content watched by the user, based on the recordeduser presence time information and operation history information.

A computer-readable program according a preferred embodiment of thepresent invention may include the steps for causing a computer toexecute: a first record control step of controlling record of userpresence time information indicating time in which a user was presentwithin a predetermined range around a display means displaying content,based on an output from a sensor detecting the user; a second recordcontrol step of controlling record of operation history informationincluding content specifying information for specifying the content astarget of operation, operation content information for indicatingcontent of operation related to display of the content by the user, andoperation time information for indicating time of the operation; andaudience-quality constituent item calculation step of calculating anaudience-quality constituent item constituting audience quality whichindicates quality of content watched by the user, based on the recordeduser presence time information and operation history information.

A recording medium according a preferred embodiment of the presentinvention may record a computer-readable program for causing a computerto execute: a first record control step of control ling record of userpresence time information indicating time in which a user was presentwithin a predetermined range around a display means displaying content,based on an output from a sensor detecting the user; a second recordcontrol step of controlling record of operation history informationincluding content specifying information for specifying the content astarget of operation, operation content information for indicatingcontent of operation related to display of the content by the user, andoperation time information for indicating time of the operation; andaudience-quality constituent item calculation step of calculating anaudience-quality constituent item constituting audience quality whichindicates quality of content watched by the user, based on the recordeduser presence time information and operation history information.

In the information processing apparatus and method, and the computerprogram in accordance with a preferred embodiment of the presentinvention, based on the output from the sensor for detecting the user,user presence time information is recorded showing the time when theuser is present within a predetermined range around the display meansfor displaying the content. An operation history information is recordedwhich includes the content specifying information for specifying thecontent to be operated, the operation content information showing thetype of operation carried out by the user with respect to displaying ofthe content, and the operation time information showing the time whenthe operation is carried out. The audience-quality constituent item iscalculated which constitutes the audience quality showing the quality ofthe content watched by the user, based on the user presence timeinformation and operation history information which are recorded.

According to a preferred embodiment of the present invention, theaudience quality can be measured. In particular, the burden is notplaced on the user, but the degree of expectation, the degree ofconcentration, and the degree of satisfaction with respect to theprogram can be acquired, and the audience quality can be measured moreefficiently.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will become more apparent from the following description ofthe presently preferred exemplary embodiments of the invention taken inconjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram showing an example of a structure of anaudience quality investigation system to which a preferred embodiment ofthe present invention is applied;

FIG. 2 is a block diagram showing an example of a structure of anaudience quality calculation function;

FIG. 3 is a block diagram showing an example of a structure of softwareinstalled in the audience quality calculation function of FIG. 2;

FIG. 4 is a block diagram showing a detailed example of a structure ofan audience-quality constituent item calculation unit of FIG. 3;

FIG. 5 is a flow chart for explaining a sensor information databasegeneration process;

FIG. 6 is a chart showing an example of a structure of a sensorinformation database;

FIG. 7 is a flow chart for explaining a user-operation-history databasegeneration process;

FIG. 8 is a chart showing an example of a structure of a user operationhistory database;

FIG. 9 is a flow chart for explaining an EPG information databasegeneration process;

FIG. 10 is a chart showing an example of a structure of an EPGinformation database;

FIG. 11 is a flow chart for explaining an audience quality calculationprocess;

FIG. 12 is a flow chart for explaining a degree-of-expectationcalculation process;

FIG. 13 is a flow chart for explaining a degree-of-concentrationcalculation process;

FIG. 14 is a flow chart for explaining a degree-of-satisfactioncalculation process;

FIG. 15 is a chart showing an example of a structure of an audiencequality database;

FIG. 16 is a flow chart for explaining a data deletion process; and

FIG. 17 is a flow chart for explaining a user-operation-history datadeletion process.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereafter, preferred embodiments of the present invention will bedescribed, and the followings are relationships between the inventiondescribed in the present specification and the preferred embodimentsaccording to the present invention. The description is for confirmingthat the preferred embodiments which support the invention recited inthe present specification is described in the present specification.Therefore, if there is a preferred embodiment which is described in thespecification and not described herein, it does not mean that thepreferred embodiment does not correspond to the invention. In contrast,if the preferred embodiment is described as one corresponding to theinvention, it does not mean that the preferred embodiment does notcorrespond to any invention other than the invention.

Further, this description does not mean the entire invention asdescribed in the present specification. In other words, this descriptiondoes not deny existence of the invention which is described in thepresent specification but not claimed in this application, i.e.,existence of the invention to be divided into a divisional applicationor to be added through amendments in the future.

The information processing apparatus (for example, an audience qualitycalculation function 21 of FIG. 2) according to a preferred embodimentof the present invention includes: the first record control means (forexample, a sensor information acquisition unit 75 of FIG. 3) forcontrolling the record of the user presence time information (forexample, a sensor information database 91 of FIG. 3) showing the timewhen the user is present in the predetermined range around the displaymeans (for example, a TV receiving set 2 of FIG. 1) for displaying thecontent, based on the output from the sensor (for example, a humandetecting sensor 4 of FIG. 1) for detecting a user; the second recordcontrol means (for example, an operation detection unit 77 of FIG. 3)for controlling the record of the operation history information (forexample, a user operation history database 92 of FIG. 3) including thecontent specifying information for specifying the content to beoperated, the operation content information showing the type ofoperation carried out by the user with respect to the display of thecontent, and the operation time information showing the time when theoperation is carried out; and the audience-quality constituent itemcalculation means (for example, an audience-quality constituent itemcalculation unit 72 of FIG. 3) for calculating the audience-qualityconstituent item which constitutes the audience quality showing thequality of the content watched by the user, based on the user presencetime information and the operation history information which arerecorded.

The information processing apparatus according to another preferredembodiment of the present invention is further provided with the thirdrecord control means (for example, network communication unit 74 of FIG.3) for acquiring the content specifying information for specifying thebroadcast content and EPG (Electric Program Guide) informationcontaining the broadcast start time and broadcast end time of thecontent, and controlling the record of the EPG information (for example,EPG information database 93 of FIG. 3), and the audience-qualityconstituent item calculation means may be arranged to calculate theaudience-quality constituent item of the content being broadcast, basedon the user presence time information, the operation historyinformation, and the EPG information.

The information processing apparatus according to another preferredembodiment of the present invention is further provided with theaudience quality generation means (for example, an audience qualitygeneration unit 73 of FIG. 3) for generating the data of the audiencequality, based on the value of the audience-quality constituent itemcalculated by the audience-quality constituent item calculation means.

In the information processing apparatus according to another preferredembodiment of the present invention, the audience-quality constituentitem calculation means may be provided with, at least, thedegree-of-expectation calculation means (for example, adegree-of-expectation calculation unit 111 of FIG. 4) for calculatingthe degree of expectation representing the degree of the userexpectation for the content; the degree-of-concentration calculationmeans (for example, a degree-of-concentration calculation unit 112 ofFIG. 4) for calculating the degree of concentration representing thedegree of concentration of the user watching the content; and thedegree-of-satisfaction calculation means (for example, adegree-of-satisfaction calculation unit 113 of FIG. 4) for calculatingthe degree of satisfaction representing the degree of satisfaction ofthe user having watched the content.

In the information processing apparatus according another preferredembodiment of the present invention, the audience quality generationmeans stores the data of the calculated audience quality into theaudience quality database (for example, an audience quality database 94of FIG. 3), and the data of the audience quality which is stored in theaudience quality database is transmitted through the network to anotherapparatus (for example, an audience quality server 7) at the presettiming (transmitted by processing of step S110 of FIG. 11, for example).

The information processing apparatus according to another preferredembodiment of the present invention is further provided with thedeletion means (for example, a data deletion unit 76 of FIG. 3) fordeleting unnecessary information among the user presence timeinformation, the operation history information, the EPG information, andthe information in the audience quality database.

The information-processing method according to another preferredembodiment of the present invention includes; the first record step (forexample, step S12 of FIG. 5) of recording the user presence timeinformation (for example, the sensor information database 91 of FIG. 3)showing the time when the user is present in the predetermined rangearound the display means (for example, the TV receiving set 2 of FIG. 1)for displaying the content, based on the output from the sensor (forexample, the human detecting sensor 4 of FIG. 1) for detecting the user;the second record step (for example, step S32 of FIG. 7) of recordingthe operation history information (for example, the user operationhistory database 92 of FIG. 3) including the content specifyinginformation for specifying the content to be operated, the operationcontent information showing the type of operation carried out by theuser with respect to the displaying of the content, and the operationtime information showing the time when the operation is carried out; andthe audience-quality constituent item calculation step (for example,step S105 of FIG. 11) of calculating the audience-quality constituentitem which constitutes the audience quality showing the quality of thecontent watched by the user, based on the user presence time informationand operation history information which are recorded.

The computer program according to another preferred embodiment of thepresent invention may cause a computer to execute: the first recordcontrol step (for example, step S12 of FIG. 5) of controlling the recordof the user presence time information (for example, the sensorinformation database 91 of FIG. 3) showing the time when the user ispresent in the predetermined range around the display means (forexample, the TV receiving set 2 of FIG. 1) for displaying the content,based on the output from the sensor (for example, the human detectingsensor 4 of FIG. 1) for detecting the user; the second record controlstep (for example, step S32 of FIG. 7) of controlling the record of theoperation history information (for example, the user operation historydatabase 92 of FIG. 3) including the content specifying information forspecifying the content to be operated, the operation content informationshowing the type of operation carried out by the user with respect tothe displaying of the content, and the operation time informationshowing the time when the operation is carried out; and theaudience-quality constituent item calculation control step (for example,step S105 of FIG. 11) of controlling the calculation of theaudience-quality constituent item which constitutes the audience qualityshowing the quality of the content watched by the user, based on theuser presence time information and operation history information whichare recorded.

The recording medium according to another preferred embodiment of thepresent invention may have a computer program recorded therein forcausing the computer cause to execute the first record control step (forexample, step S12 of FIG. 5) of controlling the record of the userpresence time information (for example, the sensor information database91 of FIG. 3) showing the time when the user is present in thepredetermined range around the display means (for example, the TVreceiving set 2 of FIG. 1) for displaying the content, based on theoutput from the sensor (for example, the human detecting sensor 4 ofFIG. 1) for detecting the user; the second record control step (forexample, step S32 of FIG. 7) of controlling the record of the operationhistory information (for example, the user operation history database 92of FIG. 3) including the content specifying information for specifyingthe content to be operated, the operation content information showingthe type of operation carried out by the user with respect to thedisplaying of the content, and the operation time information showingthe time when the operation is carried out; and the audience-qualityconstituent item calculation control step (for example, step S105 ofFIG. 11) of controlling the calculation of the audience-qualityconstituent item which constitutes the audience quality showing thequality of the content watched by the user, based on the user presencetime information and operation history information which are recorded.

Hereafter, preferred embodiments of the present invention will bedescribed with reference to the drawings. FIG. 1 is a block diagramshowing an example of a structure of an audience quality investigationsystem to which a preferred embodiment of the present invention isapplied. A TV receiving set 2 receives and displays a broadcast program,and displays the program when the program recorded on a hard diskrecorder 3 is played back. The hard disk recorder 3 is provided with anaudience quality calculation function as will be described later withreference to FIG. 2, and is connected to the TV receiving set 2 and thehuman detecting sensor 4 by a cable etc. The hard disk recorder 3records an operation history of the TV receiving set 2 and outputinformation on the human detecting sensor 4. In addition, the TVreceiving set 2 and the hard disk recorder 3 are operated by the userusing a remote controller (remote commander) 5.

The human detecting sensor 4 is constituted by an infrared sensor etc.,and outputs a signal showing that there is a person (user) if the personenters a predetermined range around the TV receiving set 2. In addition,the human detecting sensor may be constituted by other sensors which candetect the person, such as a microwave sensor, a camera, etc., or may beconstructed by a combination of a plurality of these sensors.

Further, the hard disk recorder 3 is connected to the Internet 6,communicates with a server 7 over the Internet 6, and transmits(uploads) an audience quality to the server 7 at a predetermined timing.The server 7 has a storage unit constituted by a hard disk etc., andstores information transmitted from the hard disk recorder 3.

FIG. 2 is a block diagram showing an example of a structure of anaudience quality calculation function 21. A network interface 41 isconstituted by a modem, a terminal adopter, etc., for example, andperforms a communications process through the Internet 6 etc. A storageunit 42 is constituted by a HDD (Hard Disc Drive), stores an operationhistory of the user by means of the remote controller 5, the informationoutputted from the human detecting sensor 4, and the calculated audiencequality of the program, and also stores the program (data) when theprogram is recorded.

A secondary storage unit (or secondary recording unit) 43 is constitutedby a drive equipped with a recording medium 51, such as a DVD (DigitalVersatile Disc), a Blu-Ray Disc, and copies the data of the programrecorded on the storage unit 42 (memory) to the recording medium 51according to the user's instructions.

A sensor signal acquisition unit 44 acquires the signal outputted fromthe human detecting sensor 4, and outputs information showing time whenhuman presence is detected (is present) to a general purpose processor49. In addition, when the human detecting sensor 4 is constituted by acombination of a plurality of sensors, the sensor signal acquisitionunit 44 acquires the signal outputted from the plurality of sensors.

A remote control receptor 45 receives a command transmitted from theremote controller 5, and outputs it to the general purpose processor 49.

A TV tuner 47 receives a signal of a broadcast TV program, and outputs avideo or audio signal. A capture device 46 carries out A/D conversion ofthe video or audio signal, and outputs it to the storage unit 42 as adigital data. A video card 48 carries out D/A conversion of the video oraudio digital data to output and display it on the TV receiving set 2.

The general purpose processor 49 is a processor, such as a CPU (CentralProcessing Unit) etc., and performs various types of processes based onthe command outputted from the remote control receptor 45, and software,such as a computer program installed from the recording medium to bemounted in a secondary storage apparatus (secondary recording apparatus)A memory 50 is constituted by a semiconductor memory, such as a RAM(Random Access Memory) etc., in which data etc. necessary for thegeneral purpose processor 49 to perform various types of processes areconveniently stored.

In addition, though in this example the audience quality calculationfunction 21 is arranged to be provided in the hard disk recorder 3, itmay be provided in another apparatus (for example, the TV receiving set2).

Next, an example of a functional structure of the software installed inthe audience quality calculation function 21 will be described withreference to FIG. 3. A command execution unit 71 analyzes the commandoutputted from the remote control receptor 45 etc., and outputs theinformation (command etc) to an audience-quality constituent itemcalculation unit 72.

Based on the information stored in a sensor information database 91, auser operation history database, and an EPG information database 93, anaudience-quality constituent item calculation unit 72 calculates eachvalue of a degree of expectation, a degree of concentration, and adegree of satisfaction with respect to a viewer's (user's) program, andoutputs each value of the degree of expectation, the degree ofconcentration, and the degree of satisfaction to the audience qualitygeneration unit 73.

The sensor information database 91 is a database for storing theinformation, generated when the sensor information acquisition unit 75acquires the information outputted from the sensor signal acquisitionunit 44, showing the time when human presence is detected, and isprovided in the storage unit 42. The user operation history database 92is a database for storing a user's operation history, generated suchthat a user's operation is detected by the operation detection unit 77based on the information about the command outputted from the remotecontrol receptor 45, and is provided in the storage unit. 42. The EPGinformation database 93 is a database which stores the EPG (ElectronicProgram Guide) information received by the network communication unit 74or the TV tuner 47, and is also provided in the storage unit 42.

The audience quality generation unit 73 generates a data of an audiencequality such that each value of the degree of expectation, the degree ofconcentration, and the degree of satisfaction which are calculated bythe audience-quality constituent item calculation unit 72 are madecorrespond to the program. The data is outputted to (stored in) theaudience quality database 94 which is provided in the storage unit 42.Through the network communication unit 74, the data is uploaded to theserver 7 connected to the Internet 6.

The data deletion unit 76 deletes unnecessary data out of the datastored in the sensor information database 91, the user operation historydatabase, the EPG information database 93, and the audience qualitydatabase 94.

FIG. 4 is a block diagram showing a detailed example of a structure ofthe audience-quality constituent item calculation unit 72. In thisexample, the audience-quality constituent item calculation unit 72 isconstituted by the degree-of-expectation calculation unit 111 whichcalculates the value of the degree of expectation, thedegree-of-concentration calculation unit 112 which calculates the valueof the degree of concentration, and the degree-of-satisfactioncalculation unit 113 which calculates the value of the degree ofsatisfaction. In addition, in the preferred embodiment, it is assumedthat the audience quality has at least three constituent items, thedegree of expectation which is an evaluation value before watching theprogram, the degree of concentration which is an evaluation value duringwatching the program, and the degree of satisfaction which is anevaluation value after watching the program. When calculating aconstituent item other than the above-mentioned three constituent items,a calculation unit for calculating a value of the constituent item isadded to the audience-quality constituent item calculation unit 72.

Next, with reference to a flow chart of FIG. 5, a sensor informationdatabase generation process will be described. In step S11, the sensorinformation acquisition unit 75 acquires the information (sensorinformation outputted from the human detecting sensor 4) outputted fromthe sensor signal acquisition unit 44. In step S12, the sensorinformation acquisition unit 75 stores the sensor information acquiredin step S11 into the sensor information database 91.

FIG. 6 is a chart showing an example of a structure of the thusgenerated sensor information database 91. As shown in this chart, humandetection start time which is the time when human presence starts to bedetected by the human detecting sensor 4, and human detection end timewhich is the time when the detection of the human presence is ended bythe human detecting sensor 4 are respectively recorded in the sensorinformation database 91. In this example, it follows that the presenceof a person (user) is detected within the predetermined range around theTV receiving set 2 in the interval between 10:10 and 11:35 on Jan. 30,2004. It further follows that the human presence is also detected in theinterval between 17:25 and 18:30 on Jan. 30, 2004 and the intervalbetween 21:10 and 22:35 on Jan. 30, 2004.

Next, with reference to a flow chart of FIG. 7, a user-operation-historydatabase generation process will be described. In step S31, theoperation detection unit 77 detects the user's operation, such asturn-on of a power supply, a change of a channel, etc. In step S32, theoperation detection unit 77 stores content of operation detected in stepS31 into the user operation history database 92.

FIG. 8 shows an example of a structure of the thus generated useroperation history database 92. As shown in this chart, a record numbershowing a number of a record stored in the user operation historydatabase 92, date and time when the operation is carried out, theabove-mentioned type of operation, and a watching program ID which is anID for specifying the program watched by the user are recorded in theuser operation history database 92. The first record (record No. 1)shows that instructions are issued to display the EPG (“Show EPG”) at9:30 on Jan. 25, 2004, and the EPG which introduces a programcorresponding to a program ID “0012-20040129-000017” is displayed. Thethird record shows that the power supply for the TV receiving set 2 isturned on at 10:10 on Jan. 30, 2004 (“TV Power on”), and a programcorresponding to a program ID “0003-20040130-000011” is displayed atthis time.

The fourth record shows that a program corresponding to a program ID“0003-20040130-000012” is displayed at 10:15 on Jan. 30, 2004. Sinceoperations by the user, such as a change of a channel, are not performedat this time, “NULL” is recorded in the “type of operation” column.

The fifth record shows that instructions are issued to switch to achannel 10 (“TV Channel 10”) at 10:30 on. Jan. 30, 2004, as a result aprogram corresponding to a program ID “0010-20040130-000015” is thendisplayed, the sixth and the seventh records show that operations by theuser are not performed but program ID's are changed.

The eighth record shows that the power supply for the TV receiving set 2is turned off (“TV Power off”) at 11:55 on Jan. 30, 2004. Since aprogram is not displayed at this time, “NULL” is recorded on thewatching program ID column.

The ninth record shows that the power supply for the TV receiving set 2is turned on (“TV Power on”) again at 17:30 on Jan. 30, 2004.

The tenth record shows that instructions are issued to play back theprogram stored in the hard disk recorder 3 (“HDR Play”) at 17:30 on Jan.30, 2004, as a result the program corresponding to the program ID“0012-20040129-000017” is displayed.

After that, the eleventh through the fourteenth records show that thefact that the operation of the turn-on or the turn-off of the powersupply for the TV receiving set. 2, or the switching of the channel isperformed is recorded together with the operation time and the programID of the program then displayed.

Next, with reference to a flow chart of FIG. 9, an EPG informationdatabase generation process will be described. In step S51, the networkcommunication unit 74 acquires EPG information. In step S52, the networkcommunication unit 74 stores the EPG information acquired in step S51into the EPG information database 93.

FIG. 10 is a chart showing an example of a structure of the thusgenerated EPG information database 93. As shown in this chart, theprogram ID which specifies the program, the start time and end time ofthe program, the channel through which that the program is broadcast,and a title of the program are recorded in the EPG information database93. For example, the first record shows that a program corresponding tothe program ID “0012-20040129-000017” has a title “000”, and isbroadcast through a channel 12 from 3:00 to 4:00 on Jan. 29, 2004.Subsequently, a program ID of each program, start time and end time ofthe program, a channel through which the program is broadcast, and atitle of the program are described in a similar way.

Next, with reference to a flow chart of FIG. 11, an audience qualitycalculation process using the hard disk recorder 3 (audience qualitycalculation function 21) will be described. This process may beperformed at predetermined intervals (for example, once a day), or maybe performed based on the user's instructions.

In step S101, the audience quality calculation item generation unit 72specifies a program ID of a target program based on the user operationhistory database 92. For example, in the user operation history database92 of FIG. 8, since the program watched first is the programcorresponding to the program ID “0003-20040130-000011”, the program ID“0003-20040130-000011” is specified.

In step S102, the audience quality calculation item generation unit 72acquires the start time and end time of the target program based on theEPG information database 93. As the start time of the programcorresponding to the program ID “0003-20040130-000011”, “10:00 on Jan.30, 2004” is acquired, and as the end time “10:15 on Jan. 30, 2004” isacquired from the EPG information database 93 of FIG. 10.

In step S103, the audience quality calculation item generation unit 72reads the operation history about the target program from the useroperation history database 92, and sets a value 0 to a variable N instep S104, where it is assumed that the variable N specifies a type of aconstituent item of the audience quality, and takes a value between 0and Nmax−1. Nmax is a value which is preset as a value showing a numberof the constituent items of the audience quality. For example, when theaudience quality is constituted by three constituent items, the degreeof expectation, the degree of concentration, and the degree ofsatisfaction, a value 3 is set to Nmax.

In step S105, the audience quality calculation item generation unit 72performs an audience quality constituent item (N) value computation.Details of the audience quality constituent item (N) value computationwill be described later with reference to FIGS. 12 through 14, wherebythe value of the degree of expectation, the degree of concentration, orthe degree of satisfaction of the target program is calculated. Then,the process moves to step S106.

In step S106, the audience quality calculation item generation unit 72determines whether or not the value of the variable N is smaller thanthe value Nmax. When it is determined that the value of the variable Nis smaller than the value Nmax, the process moves to step S107, thevalue of the variable N is incremented by 1, the process returns to stepS105. Thus, for example, when N=0 the value of the degree of expectationis computed in step S105. When N=1, the value of degree of concentrationis computed in step S105. When N=2, the value of the degree ofsatisfaction is computed in step S105. When N=3, it is determined instep S106 that the value of the variable N is not smaller than the valueNmax, and the process moves to step S108.

In step S108, the audience quality generation unit 73 stores an audiencequality data into the audience quality database 94, the data beingmatched to the target program. At this time, being matched to the targetprogram's program ID “0003-20040130-000011”, each value of the degree ofexpectation, the degree of concentration, and the degree of satisfactionwhich are computed by way of the audience quality constituent item (N)value computation in step 6105 is stored as the audience quality data.

In step 6109, the audience quality generation unit 73 determines whetherall the programs watched by the user are checked or not. When it isdetermined not to have checked all the programs yet, the process returnsto step S1. In the user operation history database 92 of FIG. 8, sincethe program which is watched following the program corresponding to theprogram ID “0003-20040130-000011” is a program corresponding to theprogram ID “0003-20040130-000012”, the target program's program ID isspecified as the ID “0003-20040130-000012” in step S101, and thesubsequent processes are repeatedly performed.

In step S109, when it is determined that all the programs watched by theuser are checked, the process moves to step S110, and the networkcommunication unit 74 transmits and uploads the audience quality datastored in step S108 to the audience quality server 7 through theInternet 6.

Thus, the audience quality of the program watched by the user iscalculated. In the above-mentioned audience quality calculation process,since the user does not have to input the evaluation or does not have toperform a special operation, the audience quality can be automaticallycalculated, without placing a burden on the user. Further, since theuser does not particularly need to wear a sensor etc. in the case ofwatching the program, the user can comfortably enjoy watching theprogram.

Next, with reference to a flow chart of FIG. 12, thedegree-of-expectation calculation process will be described which is afirst example of the audience quality constituent item (N) valuecomputation of step S105 of FIG. 11. This process is performed in stepS105, when the value of the variable N is 0 in the audience qualitycalculation process of FIG. 11.

In step S131, the degree-of-expectation calculation unit 111 sets aninitial value 0 to a variable “e” showing the degree of expectation.

In step S132, the degree-of-expectation calculation unit 111 determineswhether or not the EPG of the target program was displayed in the pastbased on the user operation history database 92. At this event, thedegree-of-expectation calculation unit 111 searches the user operationhistory database 92 of FIG. 8 for a history of the display of EPG, i.e.a record (first record) in which the type of operation is “Show EPG”,and checks whether or not the watching program ID of the record is equalto the target program's program ID.

Now, it is assumed that the target program is a program corresponding tothe program ID “0003-20040130-000011”. Unlike the target program'sprogram ID, since the watching program ID of the first record of FIG. 8in which the type of operation is “Show EPG” is not another record inwhich the type of operation is “Show EPG”, it is determined in step S132that the EPG of the target program was not displayed in the past, andthe process in step S133 to be described later is skipped. Withoutincrementing the value of the variable “e”, the process moves to stepS134.

On the other hand, in step S132, when it is determined that the EPG ofthe target program was displayed in the past, the process moves to stepS133 and the degree-of-expectation calculation unit 111 increments thevalue of the variable “e” by 1. Since it is considered that the user wasinterested in the program when the EPG introducing the target programwas displayed in the past, a point is added to the degree ofexpectation.

In step S134, the degree-of-expectation calculation unit 111 determineswhether or not the watching of the target program is watching by way ofplayback of a recorded content (program). At this time, thedegree-of-expectation calculation unit 111 searches the user operationhistory database 92 of FIG. 8 for a playback history of the recordedcontent i.e. a record (tenth record) in which the type of operation is“HDR Play”, and checks whether or not the watching program ID of therecord is equal to the target program's program ID. Now, it is assumedthat the target program is a program corresponding to the program ID“0003-20040130-000011”. Unlike the target program's program ID, sincethe watching program ID of the tenth record of FIG. 8 in which the typeof operation is “HDR Play” is not another record in which the type ofoperation is “HDR Play”, in step S134, it is determined not to be thewatching by way of playback of the recorded content (program), and theprocess moves to step S135.

In step S135, the degree-of-expectation calculation unit 111 determineswhether or not the channel has been used for watching before the targetprogram broadcast starts. For example, now, assuming that the targetprogram is a program corresponding to the program ID“0003-20040130-000011”, the degree-of-expectation calculation unit 111searches the EPG information database 93 of FIG. 10 for a record inwhich a program ID is “0003-20040130-000011”. Based on the record, itacquires the start time (in this case, 10:00 on Jan. 30, 2004) of thetarget program. Further, the record in which the watching program ID is“0003-20040130-000011” is retrieved from the user operation historydatabase 92 of FIG. 8. Based on the record (in this case, third record),it is confirmed that the target program is watched from 10:10 on Jan.30, 2004. Therefore, in step S135, it is determined that the channel hasnot been used for watching before the target program broadcast starts.The process of step S136 to be described later is skipped, the value ofthe variable “e” is not incremented, and the process moves to step S137.

On the other hand, in step S135, when it is determined that the channelhas been used for watching before the target program broadcast starts,the process moves to step S136. The degree-of-expectation calculationunit 111 increments the value of the variable “e” by 1. Since it isconsidered that the user has been waiting for the program broadcast tostart when it is determined that the channel has been used for watchingbefore the target program broadcast starts, a point is added to thedegree of expectation.

In step S137, the degree-of-expectation calculation unit 111 determineswhether or not a person has been detected before the target programbroadcast starts. At this time, the degree-of-expectation calculationunit 111 compares the broadcast start time of the target program withthe time when human presence is detected by the human detecting sensor 4with reference to the sensor information database 91 of FIG. 5. Now,supposing the target program is a program corresponding to the program.ID “0003-20040130-000011”, it is seen that the start time of the targetprogram is 10:00 on Jan. 30, 2004 from the EPG information database 93of FIG. 10, and that, from the sensor information database 91 of FIG. 6,the person has been detected since 10:10 on Jan. 30, 2004. Therefore, instep S137, it is determined that the person has not been detected beforethe target program broadcast starts, and the process moves to step S139.

On the other hand, in step S137, when it is determined that the personwas detected before the target program broadcast starts, the processmoves to step S138, and the degree-of-expectation calculation unit 111increments the value of the variable “e” by 1. Since it is consideredthat the user has been waiting for the program broadcast to start whenit is determined that the human presence was detected before the targetprogram broadcast starts, a point is added to the degree of expectation.

In step S139, the degree-of-expectation calculation unit 111 determineswhether or not a recording reservation of the target program isperformed. At this event, the degree-of-expectation calculation unit 111searches the user operation, history database 92 of FIG. 8 for a historyof content (program) recording reservations i.e., a record (notdescribed in this example) in which the type of operation is “HDR Rec”,and checks whether or not the watching program ID of the record is equalto the target program's program ID. Since the record (history of contentrecording) in which the type of operation is “HDR Rec” does not nowremain in the user operation history database 92 of FIG. 8, it isdetermined in step S139 that the recording reservation of the targetprogram has not been made. The process of step S140 as will be describedlater is skipped, the value of the variable “e” is not incremented, andthe process moves to step S142.

On the other hand, in step S139, when it is determined that therecording reservation of the target program has been made, the processmoves to step S140, and the degree-of-expectation calculation unit 111increments the value of the variable “e” by 1. Since it is consideredthat the user shows expectation to the program when it is determinedthat the recording reservation of the target program is made, a point isadded to the degree of expectation.

Further, in step S134, when it is determined that watching the targetprogram is watching by way of playback of the recorded content(program), the process moves to step S141, the degree-of-expectationcalculation unit 111 increments the value of the variable “e” by 3, andthe process moves to step S142. Since it is considered that the user ishighly interested in the program when it is determined that the targetprogram is watched by way of playback of the recorded content (program)points of the degree of expectation are considerably increased.

In step S142, the degree-of-expectation calculation unit 111 divides thevalue of the variable “e” by 4, and normalizes it. Now, the degree ofexpectation is e=0/4=0 when the target program is a programcorresponding to the program ID “0003-20040130-000011”. Normalization inthis way allows the value of the degree of expectation to be between 0and 1, and it is possible to easily generate the data of audiencequality, compare the data, etc.

Thus, the degree of expectation which is one of the constituent items ofaudience quality is calculated. The degree of expectation is used as aconstituent item of the audience quality showing how much expectationthere is with respect to the content before the user watches it.

Next, with reference to a flow chart of FIG. 13, adegree-of-concentration calculation process which is a second example ofthe audience quality constituent item (N) value computation of step S105of FIG. 11 will be described. In the audience quality calculationprocess of FIG. 11, this process is performed in step S105, when thevalue of the variable N is 1.

In step S161, the degree-of-concentration calculation unit 112 sets aninitial value 0 as a variable “c” showing the degree of concentration.

In step S162, the degree-of-concentration calculation unit 112 detectsCM broadcasting times within the target program. Although it is thoughtthat the detection of the CM broadcasting times may be carried out byadopting various methods, it is assumed that, in this case, times (starttime and end time) when a CM is broadcast within a broadcast program aredetected based on stereo or monophonic broadcasting periods of time, forexample, and such times are stored beforehand in the storage unit 42.

In step S163, the degree-of-concentration calculation unit 112determines whether or not the watching of the target program is watchingby way of playback of the recorded content (program). At this event, thedegree-of-concentration calculation unit 112 searches the user operationhistory database 92 of FIG. 8 for the playback history of recordedcontent, and checks whether or not the watching program ID of the recordis equal to the target program's program ID. For example, it is nowassumed that the target program is a program corresponding to theprogram ID “0003-20040130-000011”. Unlike the target program's program.ID, since the watching program ID of the tenth record of FIG. 1 is 8 inwhich the type of operation is “HDR Play”, is not another record inwhich the type of operation is “HDR Play”, it is determined in step S163that it is not the watching by way of playback of recorded content(program), and the process moves to step S164.

In step S164, the degree-of-concentration calculation unit 112calculates a zapping time t2 within the target program. The zapping timet2 (minutes) is the time when the program of another channel isdisplayed during watching the target program (under broadcast), and iscalculated based on the record of the user operation history database92. However, even if the program of another channel is displayed duringwatching the target program (under broadcast), when the times overlapwith the CM broadcasting times detected in step S162, they are notincluded in the zapping time t2.

Now, supposing the target program is a program corresponding to theprogram ID “0003-20040130-000011”, according to the user historydatabase 92 of FIG. 8, the program of another channel is not displayedduring broadcast of the program (according to the SPG informationdatabase 93 of FIG. 10, the start time and end time of the record inwhich the program ID is “0003-20040130-000011” are respectively 10:00and 10:15 on Jan. 30, 2004), so that the degree-of-concentrationcalculation unit 112 calculates the zapping time t2, which results in 0.

In step S165, the degree-of-concentration calculation unit 112 subtractsthe zapping time t2 from the variable “c”. Since it is considered thatthe longer the zapping time is, the less the user is concentrated on thetarget program, points of degree of concentration are subtracted by theamount of zapping time, for example.

On the other hand, in step S163, when it is determined that the watchingof the target program is watching by way of playback of the recordedcontent (program), the process moves to step S166 and thedegree-of-concentration calculation unit 112 calculates the time t3(minutes) skipped using fast forward. For example, it is assumed thatthe time t3 skipped using fast forward is calculated based on theoperation history recorded in the user history database 92. However,even if skipped using fast forward, when the times overlap with the CMbroadcasting times detected in step S162, they are not included in thetime t3.

In step S167, the degree-of-concentration calculation unit 112 subtractsthe time t3 skipped using fast forward from the variable “c”. Since itis considered that the longer the skipped time by way of fast forwardis, the less the user is concentrated on the target program, points ofdegree of concentration are subtracted by the amount of skipped time.

In step S168, the degree-of-concentration calculation unit 112 adds abroadcasting time T (minutes) of the target program to the variable “c”.Supposing the target program is the program corresponding to the programID “0003-20040130-000011”, the broadcasting time T of the program is 15minutes, which gives c=0+15 (minutes).

In step S169, the degree-of-concentration calculation unit 112 dividesthe value of the variable “c” by the broadcasting time T of the program,and normalizes it. When the target program is the program correspondingto the program ID “0003-20040130-000011”, the degree of expectation isc=15/15=1.

Thus, the degree of concentration which is one of the constituent itemsof the audience quality is calculated. The degree of concentration isused as a constituent item of the audience quality showing how much theuser is concentrated on watching the content.

Next, with reference to a flow chart of FIG. 14, adegree-of-satisfaction calculation process which is a third example ofthe audience quality constituent item (N) value computation of step S105of FIG. 11 will be described. For example, in step S105 this process isperformed in the audience quality calculation process of FIG. 11, whenthe value of the variable N is 2.

In step S191, the degree-of-satisfaction calculation unit 113 sets aninitial value 0 as a variable “s” showing the degree of satisfaction.

In step S192, the degree-of-satisfaction calculation unit 113 determineswhether or not the watching of the target program is watching by way ofplayback of the recorded content (program). At this time, thedegree-of-satisfaction calculation unit 113 searches the user operationhistory database 92 of FIG. 8 for the playback history of recordedcontent, and checks whether or not the watching program ID of the recordis equal to the target program's program ID. For example, it is nowassumed that the target program is the program corresponding to theprogram ID “0003-20040130-000011” Since the watching program ID of thetenth record of FIG. 8 in which the type of operation is “HDR Play” isdifferent from the target programs program. ID, it is determined not tobe the watching by way of playback of recorded content (program) in stepS192, and the process moves to step S193.

In step S193, the degree-of-satisfaction calculation unit. 113determines whether or not the target program is watched until m-minutesbefore the end time of the target program. Here, the value of m ispreset. For example, it is set to 1 (minute).

Now, it is assumed that the target program is the program correspondingto the program ID “0003-20040130-000011”. According to the user historydatabase 92 of FIG. 8, based on the third record in which the watchingprogram ID is “0003-20040130-000011” and the following fourth record,the degree-of-satisfaction calculation unit 113 checks that the targetprogram was watched until 10:15 on. Jan. 30, 2004, which is the end timeof the target program. Therefore, in step S193, the target program isdetermined to have been watched until one minute before the end time ofthe target program, the process moves to step S194, and thedegree-of-satisfaction calculation unit 113 increments the value of thevariable “s” by 1. When the target program is determined to have beenwatched until one minute before end time, since it is considered thatthe user was substantially watching the program until the last time, andthe user was satisfied with the program, then a point is added to thedegree of satisfaction.

On the other hand, in step S193, when the target program is determinednot to have been watched until m-minutes before the end time of thetarget program, the process of step S194 is skipped, the value of thevariable “s” is not incremented, and the process moves to step S195.

In step S195, the degree-of-satisfaction calculation unit 113 determineswhether or not human presence is detected when the target program isended. At this event, the degree-of-satisfaction calculation unit 113compares the broadcast end time of the target program with the time whenhuman presence is detected by the human detecting sensor 4 withreference to the sensor information database 91 of FIG. 6.

Now, supposing the target program is the program corresponding to theprogram ID “0003-20040130-00001107”, the broadcast end time of thetarget program is 10:15 on Jan. 30, 2004, and the sensor informationdatabase 91 of FIG. 6 shows that human presence is detected in theinterval between 10:10 and 11:55 on Jan. 30, 2004. Therefore, in stepS195, it is determined that human presence is detected when thebroadcast of the target program is ended, the process moves to stepS196, and the degree-of-satisfaction determination unit 113 incrementsthe value of the variable “s” by 1. Since it is considered that the useris satisfied with the program when it is determined that human presenceis detected at the end of the broadcast, a point is added to the degreeof satisfaction.

On the other hand, in step S195, when it is determined that the personis not detected at the end of the target program, the process of stepS196 is skipped, the value of the variable “s” is not incremented, andthe process moves to step S197.

In step S197, the degree-of-satisfaction calculation unit 113 dividesthe value of the variable “s” by 2, and normalizes it. When the targetprogram is the program corresponding to the program ID“0003-20040130-000011”, the degree of satisfaction is s=(1+1)/2=1.

On the other hand, since the watching program ID of the tenth record ofthe user operation history database 92 of FIG. 8 is equal to the targetprogram's program ID when, for example, the target program is theprogram corresponding to the program ID “0012-20040129-000017”, it isdetermined in step S92 that it is watching by way of playback of therecorded content (program), and the process moves to step S198.

In step S198, the degree-of-satisfaction calculation unit 113 determineswhether or not it was played back until m-minutes before the targetprogram was ended. Here, it is assumed that m is also preset as 1(minute).

In this case, according to the EPG information database 93 of FIG. 10,the degree-of-satisfaction calculation unit 113 checks that the targetprogram (record which has the program ID “0012-20040129-000017”) is aone-hour program whose start time is 3:00 on Jan. 29, 2004 and whose endtime is 4:00. Further, according to the user operation history database92 (record in which the watching program ID is “0012-20040129-000017”and the type of operation is “HDR Play”) of FIG. 8, it is confirmed thatthe target program playback was started at 17:30 on Jan. 30, 2004 andplayed back until the power supply for the TV receiving set 2 was turnedoff at 18:30 on Jan. 30, 2004.

Therefore, it is determined that the target program was played back forone hour until one minute before the target program was ended in stepS198, the process moves to step S199, and the degree-of-satisfactiondetermination unit 113 increments the value of the variable “s” by 1.Since it is considered that the user was substantially watching theprogram until the last time and the user was satisfied with the programwhen it is determined to have been played back one minute before thetarget program was ended, a point is added to the degree ofsatisfaction.

On the other hand, in step S198, when it is determined not to have beenplayed back m-minutes before the target program was ended, the processof step S199 is skipped, the value of the variable “s” is notincremented, and the process moves to step S200.

In step S200, the degree-of-satisfaction calculation unit 113 determineswhether or not human presence is detected at the end of the targetprogram. At this time, with reference to the sensor information database91 of FIG. 6, the degree-of-satisfaction calculation unit 113 comparesthe end time of the target program with the time when human presence isdetected by the human detecting sensor 4.

Now, supposing the target program is a program corresponding to theprogram ID “0012-20040129-000017”, the (playback) end time of the targetprogram is 18:30 on Jan. 30, 2004, and the degree-of-satisfactioncalculation unit 113 checks that human presence is detected in theinterval between 17:15 and 18:30 on Jan. 30, 2004 according to thesensor information database 91 of FIG. 6. Therefore, in step S200, it isdetermined that human presence is detected when the target program isended, the process moves to step S201, and the degree-of-satisfactioncalculation unit 113 increments the value of the variable “s” by 1.Since it is considered that the user is satisfied with the program whenit is determined that human presence is detected at the end of theplayback of the program, a point is added to the degree of satisfaction.

On the other hand, in step S200, when it is determined that the personis not detected at the end of the target program, the process of stepS201 is skipped, the value of the variable “s” is not incremented, andthe process moves to step S202.

In step S202, the degree-of-satisfaction calculation unit 113 determineswhether or not the recorded target program is deleted. In this case, thedegree-of-satisfaction calculation unit 113 searches the user operationhistory database 92 of FIG. 8, and checks that a record of the operationhistory indicating that the program of the program ID“0012-20040129-000017” is deleted is not recorded. Therefore, in stepS202, it is determined that the recorded target program is not deleted,the process moves to step S203, and the degree-of-satisfactioncalculation unit 113 increments the value of the variable “s” by 1.Since it is considered that the user is satisfied with the program whenit is determined that the recorded target program is not deleted, apoint is added to the degree of satisfaction.

On the other hand, when the record of the operation history (althoughnot described here, type of operation “HDR del”) indicating that thetarget program is deleted is recorded in the user operation historydatabase 92 of FIG. 8, it is determined in step S202 that the recordedtarget program is deleted, the process of step S203 is skipped, thevalue of the variable “s” is not incremented, and the process moves tostep S204.

In step S204, the degree-of-satisfaction calculation unit 113 determineswhether or not the recorded target program is saved in a recordingmedium, such as DVD. In this case, the degree-of-satisfactioncalculation unit. 113 searches the user operation history database 92 ofFIG. 8, and checks that a record of the operation history indicatingthat the program of the program ID “0012-20040129-000017” is saved inthe recording medium is not recorded. Therefore, in step S204, it isdetermined that the recorded target program is not saved in therecording medium, the process of step S205 as will be described later isskipped, the value of the variable “s” is not incremented, and theprocess moves to step S206.

On the other hand, when the record of an operation history (although notdescribed here, type of operation “HDR copy”) indicating that thetarget′ program is saved in the recording medium is recorded, it isdetermined in step S204 that the recorded target program is saved in therecording medium, the process moves to step S205, and thedegree-of-satisfaction calculation unit 113 increments the value of thevariable “s” by 1. Since it is considered that the user is satisfiedwith the program when it is determined that the recorded target programis saved in the recording media, such as DVD, and a point is added tothe degree of satisfaction.

In step S206, the degree-of-satisfaction calculation unit 113 dividesthe value of s by 4, and normalizes it. When the target program is theprogram corresponding to the program ID “0012-20040129-000017”, thedegree of satisfaction is s=(1+1+1+0)/4=0.75.

In this way, the degree of satisfaction which is one of the constituentitems of audience quality is calculated. The degree of concentration isused as a constituent item of the audience quality showing how much theuser is satisfied after watching the content.

In step S108 of FIG. 11, each value of the thus calculated degree ofexpectation, degree of concentration, and degree of satisfaction ismatched to the target program's program ID, and is stored in theaudience quality database 94 as the audience quality data.

FIG. 15 is a chart showing an example of a structure of the audiencequality database 94. As shown in this chart, the audience qualitydatabase 94 is constituted by the program ID and the audience-qualityconstituent item (N) values matched to the program. ID, (N=0, 1, 2, . .. ). Here, an audience-quality constituent item (0) value represents avalue of the degree of expectation, an audience-quality constituent item(1) value represents a value of the degree of concentration, and anaudience-quality constituent item (2) value represents a value of thedegree of satisfaction. When the audience quality (data) has anaudience-quality constituent item other than the degree of expectation,the degree of satisfaction, and the degree of concentration, it isrecorded in the audience quality database 94 as an audience-qualityconstituent item (3) value, an audience-quality constituent item (4)value, and so on.

In this example, the values (the audience-quality constituent item (0)values, the audience-quality constituent item (1) values, and theaudience-quality constituent item (2) values) of the degrees ofexpectation, the degrees of concentration, and the degrees ofsatisfaction of the programs corresponding to the program ID's“0003-20040130-000011” and “0003-20040130-000012”, are calculated andrecorded with reference to FIGS. 12 through 14, as described above.

Then in step S110 of FIG. 11, the audience quality data recorded on theaudience quality database 94 are transmitted to the audience qualityserver 7, and uploaded.

The so generated audience quality data are acquired by a TV station andthe like, from the audience quality server 7 through the Internet 6, forexample, and used for evaluation, description, etc. of the program.Further, when the hard disk recorder 3 has the function for recommendinga program to the user, the audience quality data can also be used ascriteria for selecting the program to recommend.

By the way, since the capacity of the storage unit 42 is limited, thedata stored in the above-mentioned sensor information database 91, theuser operation history information database 92, the EPG informationdatabase 93, and the audience quality database 94 are deleted by way ofa data deletion process as will be described later, after the audiencequality is calculated. The data deletion process will be described withreference to a flow chart of FIG. 16. For example, this process isperformed once (at midnight etc.) a day, after the audience quality iscalculated by the audience quality calculation process of FIG. 11.

In step S251, the data deletion unit 76 deletes a data of the sensorinformation database 91. After calculating the audience quality of theday, the data of the sensor information database 91 is deleted, since itis not particularly used.

In step S252, the data deletion unit 76 performs auser-operation-history data deletion process as will be described laterwith reference to FIG. 17, whereby a data which is not particularly usedis deleted from the user operation history database 92 after calculatingthe audience quality of the day.

In step S253, the data deletion unit. 76 deletes the data of the EPGinformation database 93 except for the record of EPG of the recordedcontent (program).

In step S254, the data deletion unit 76 deletes the data of the audiencequality database 94. As described above, the audience quality datarecorded on the audience quality database 94 is transmitted to theaudience quality server 7 in step S110 of FIG. 11, and uploaded, thendeleted. In addition, since the audience quality data can also be usedas criteria for selecting the program to recommend when the hard diskrecorder 3 has the function for recommending a program to the user, thedata of the audience quality database 94 may be arranged to be deletedexcept for the audience quality data of the recorded program in stepS254.

Thus, the unnecessary data is deleted from the data stored in thestorage unit 42.

Next, with reference to a flow chart of FIG. 17, auser-operation-history data deletion process of step S252 of FIG. 16will be described in detail.

In step S271, the data deletion unit 76 sets an initial value 0 as avariable “i”. In step S142, the data deletion unit 76 determines whetheror not the variable “i” is smaller than a value iMax. When it isdetermined that the variable “i” is smaller than the value iMax, theprocess moves to step S273. In addition, it is assumed that the valueiMax is the maximum value of the data (record) of the operation historyrecorded in the user operation history database 92.

In step S273 the data deletion unit 76 determines whether or not awatching program (program corresponding to the program ID) of the i-threcord in the user operation history database 92 is recorded. When it isdetermined that the watching program of the i-th record is not recorded,the process moves to step S145, and the data deletion unit 76 deletesthe i-th record from the user operation history database 92. Aftercalculating the audience quality of the day, the operation history ofthe content which is not recorded is deleted, since it is notparticularly used.

On the other hand, when it is determined in step S273 that the watchingprogram of the i-th record is recorded, the data deletion unit 76determines in step S274 whether or not the type of operation of the i-threcord is “ShowEPG”.

In step S274, when it is determined that the type of operation of thei-th record is “ShowEPG”, the process moves to step S276 and incrementsthe value of the variable “i” by 1. Even after calculating the audiencequality of the day, the record in which the watching program is therecorded content (program) and the type of operation is “ShowEPG” is notdeleted, since it may be used in the degree-of-expectation calculationprocess (step S132 of FIG. 12).

On the other hand, in step S274, when it is determined that the type ofoperation of the i-th record is not “ShowEPG”, the process moves to stepS275. The data deletion unit 76 deletes the i-th record, and the processmoves to step S276. Even if the watching program is the recorded content(program), the record in which the type of operation is not “ShowEPG” isdeleted after calculating the audience quality of the day, since it isnot particularly used.

After processing in step S276, the process returns to step S272, and thesubsequent processes are repeatedly performed. Then, in step S142, whenit is determined that the variable “i” is not smaller than the valueiMax, the user-operation-history data deletion process is ended.

Thus, after checking all the records (data) of the user operationhistory database 92 and calculating the audience quality of the day, therecord that is not particularly used is deleted.

The above-mentioned series of processes may be realized either by meansof hardware or by way of software. When the above-mentioned series ofprocesses are executed by the software, a program which constitutes thesoftware is installed through the network or the recording medium 51consisting of a removable media etc.

In addition, in the present specification, the steps of performing theabove-mentioned series of processes may include not only the processesperformed in sequence in accordance with the described order but alsothe processes performed in parallel or individually, so that the stepsmay not necessarily be processed in sequence.

Therefore, although the preferred embodiments of the present inventionare particularly described above, the present invention is not limitedto the above-mentioned preferred embodiments. It will be obvious tothose skilled in the art that various changes, modifications,combinations, sub combinations and alterations may be made depending ondesign requirements and other factors insofar as they are within thescope of the appended claims or equivalents thereof.

1.-10. (canceled)
 11. An information processing apparatus comprising: aprocessor configured to: receive sensor data; create user profileinformation based on first information and the sensor data includingtime series information, the first information includes operationhistory information for one or more contents using a device terminal;and provide recommended information using the created user profileinformation to device terminal.
 12. The information processing apparatusof claim 11, wherein the time series information includes user presencetime information indicating a time at which a user enters a range of adisplay of the device terminal.
 13. The information processing apparatusof claim 11, wherein the operation history information includes: contentspecifying information for specifying the content as a target ofoperation, operation content information for indicating a content ofoperation, the content of operation related to a display of the content,and operation time information for indicating a time of the operation.14. The information processing apparatus of claim 13, further comprisinga record controller configured for: acquiring the content specifyinginformation, acquiring EPG information containing a broadcast start timeand a broadcast end time of the content, and controlling a recording ofthe EPG information.
 15. The information processing apparatus of claim14, further comprising: a calculating unit configured for calculatingaudience-quality constituent item according to the user presence timeinformation, the operation history information, and the EPG information.16. The information processing apparatus of claim 15, further comprisingan audience quality generation unit configured for generating data ofaudience quality according to a value of the audience-qualityconstituent item.
 17. The information processing apparatus of claim 16,wherein the audience quality generation unit is further configured forstoring the data of the audience quality into an audience qualitydatabase, and the data of the audience quality stored in the audiencequality database is transmitted to another apparatus via a network at apreset timing.
 18. The information processing apparatus of claim 17,further comprising a deletion unit configured for deleting unnecessaryinformation among the user presence time information, operation historyinformation, EPG information, and information of the audience qualitydatabase.
 19. A computer-implemented information processing methodcomprising the following operations performed by at least one processor:receiving sensor data; creating user profile information based on firstinformation and the sensor data including time series information, thefirst information includes operation history information for one or morecontents using a device terminal; and providing recommended informationusing the created user profile information to device terminal.
 20. Thecomputer-implemented information processing method of claim 19, whereinthe time series information includes user presence time informationindicating a time at which a user enters a range of a display of thedevice terminal.
 21. The computer-implemented information processingmethod of claim 19, wherein the operation history information includes:content specifying information for specifying the content as a target ofoperation; operation content information for indicating a content ofoperation, the content of operation related to a display of the content;and operation time information for indicating a time of the operation.22. The computer-implemented information processing method of claim 21,further comprising: acquiring the content specifying information;acquiring EPG information containing a broadcast start time and abroadcast end time of the content; and controlling a recording of theEPG information.
 23. The computer-implemented information processingmethod of claim 22, further comprising: calculating audience-qualityconstituent item according to the user presence time information, theoperation history information, and the EPG information.
 24. Thecomputer-implemented information processing method of claim 23, furthercomprising: generating data of audience quality according to a value ofthe audience-quality constituent item.
 25. The computer-implementedinformation processing method of claim 24, further comprising: storingthe data of the audience quality into an audience quality database; andtransmitting the data of the audience quality stored in the audiencequality database to another apparatus via a network at a preset timing.26. The computer-implemented information processing method of claim 25,further comprising: deleting unnecessary information among the userpresence time information, operation history information, EPGinformation, and information of the audience quality database.
 27. Anon-transitory computer-readable medium encoded with instructions that,when executed by at least one processor, cause the processor to performan information processing method comprising: receiving sensor data;creating user profile information based on first information and thesensor data including time series information, the first informationincludes operation history information for one or more contents using adevice terminal; and providing recommended information using the createduser profile information to device terminal.
 28. The non-transitorycomputer-readable medium of claim 27, wherein the time seriesinformation includes user presence time information indicating a time atwhich a user enters a range of a display of the device terminal.
 29. Thenon-transitory computer-readable medium of claim 28, wherein theoperation history information includes: content specifying informationfor specifying the content as a target of operation, operation contentinformation for indicating a content of operation, the content ofoperation related to a display of the content, and operation timeinformation for indicating a time of the operation.
 30. Thenon-transitory computer-readable medium of claim 29, further comprising:acquiring the content specifying information; acquiring EPG informationcontaining a broadcast start time and a broadcast end time of thecontent; and controlling a recording of the EPG information.